Abstract

In this decade, with the rise of data science accompanying the growth of e-commerce, many technologies have been developed. An example of these technologies is Blockchain, which has appeared to overcome security problems potentially. This research assesses Blockchain's implementation in supply chains through a methodology that uses deep learning and agent-based simulation. A case study was utilized to observe and validate research developments. The unique method predicts intrusions by using deep learning, and agent-based modeling reproduces artificial but convincing agents (e.g., customers, companies, hackers, and cyber pirates) in a computer-generated market. Trust and other relationships are systematically captured to represent Blockchain additions. Once again, the agent-based simulation model's environment permits hypothetical interactions and emergent features by coordinating supply and demand for business-to-consumer e-commerce events. The case study based on a real environment shows that the proposed method can determine the feasibility of the business model and Blockchain implementation's potential contributions.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2020

Semester

Fall

Advisor

Rabelo, Luis

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0008361; DP0023798

URL

https://purls.library.ucf.edu/go/DP0023798

Language

English

Release Date

December 2021

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS